{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting networkx==2.5\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/cd/dc52755d30ba41c60243235460961fc28022e5b6731f16c268667625baea/networkx-2.5-py3-none-any.whl (1.6MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6MB 10.0MB/s eta 0:00:01     |███████████████▍                | 778kB 10.0MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from networkx==2.5) (4.4.2)\n",
      "Installing collected packages: networkx\n",
      "Successfully installed networkx-2.5\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting matplotlib==3.2.2\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8d/b5/2309a0308d22cb8955c5140ad47080d990244df626877a86b86cebf153bc/matplotlib-3.2.2-cp38-cp38-macosx_10_9_x86_64.whl (12.5MB)\n",
      "\u001b[K     |████████████████████████████████| 12.5MB 2.6MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from matplotlib==3.2.2) (2.4.7)\n",
      "Collecting cycler>=0.10 (from matplotlib==3.2.2)\n",
      "  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: numpy>=1.11 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from matplotlib==3.2.2) (1.19.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from matplotlib==3.2.2) (2.8.1)\n",
      "Collecting kiwisolver>=1.0.1 (from matplotlib==3.2.2)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/89/cdd752da48b66f31a0732902cd9800e6d232ce65f6cc4e83f9d9d2bd99aa/kiwisolver-1.3.1-cp38-cp38-macosx_10_9_x86_64.whl (61kB)\n",
      "\u001b[K     |████████████████████████████████| 71kB 6.6MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from cycler>=0.10->matplotlib==3.2.2) (1.15.0)\n",
      "Installing collected packages: cycler, kiwisolver, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.2.2\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting pandas==1.1.3\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/09/3c2ee77531dc30d4265d1f148d08d283de2d57fdd00745a9b367137d54ac/pandas-1.1.3-cp38-cp38-macosx_10_9_x86_64.whl (10.1MB)\n",
      "\u001b[K     |████████████████████████████████| 10.1MB 5.1MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.15.4 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from pandas==1.1.3) (1.19.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from pandas==1.1.3) (2.8.1)\n",
      "Collecting pytz>=2017.2 (from pandas==1.1.3)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/94/784178ca5dd892a98f113cdd923372024dc04b8d40abe77ca76b5fb90ca6/pytz-2021.1-py2.py3-none-any.whl (510kB)\n",
      "\u001b[K     |████████████████████████████████| 512kB 9.4MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six>=1.5 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas==1.1.3) (1.15.0)\n",
      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-1.1.3 pytz-2021.1\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting scipy==1.6.2\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/c9/8d034cd5e0543e15e99a35836428c70618b87aff1fb80656094fba23c659/scipy-1.6.2-cp38-cp38-macosx_10_9_x86_64.whl (30.8MB)\n",
      "\u001b[K     |████████████████████████████████| 30.8MB 2.6MB/s eta 0:00:01     |████████████████████████▌       | 23.6MB 2.4MB/s eta 0:00:03     |███████████████████████████████▏| 29.9MB 2.6MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy<1.23.0,>=1.16.5 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from scipy==1.6.2) (1.19.1)\n",
      "\u001b[31mERROR: tensorflow 2.2.0 has requirement scipy==1.4.1; python_version >= \"3\", but you'll have scipy 1.6.2 which is incompatible.\u001b[0m\n",
      "Installing collected packages: scipy\n",
      "  Found existing installation: scipy 1.4.1\n",
      "    Uninstalling scipy-1.4.1:\n",
      "      Successfully uninstalled scipy-1.4.1\n",
      "Successfully installed scipy-1.6.2\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install networkx==2.5 \n",
    "!pip install matplotlib==3.2.2 \n",
    "!pip install pandas==1.1.3 \n",
    "!pip install scipy==1.6.2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "uKraKcP_lyqO"
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "default_edge_color = 'gray'\n",
    "default_node_color = '#407cc9'\n",
    "enhanced_node_color = '#f5b042'\n",
    "enhanced_edge_color = '#cc2f04'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ci5ithlNjeCM"
   },
   "source": [
    "## Chapter 2.2: Graph properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "rNOJ_ofKpb94"
   },
   "outputs": [],
   "source": [
    "# draw a simple graph\n",
    "def draw_graph(G, node_names={}, filename=None, node_size=50):\n",
    "    pos_nodes = nx.spring_layout(G)\n",
    "    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray')\n",
    "    \n",
    "    pos_attrs = {}\n",
    "    for node, coords in pos_nodes.items():\n",
    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
    "        \n",
    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')\n",
    "    \n",
    "    plt.axis('off')\n",
    "    axis = plt.gca()\n",
    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
    "    \n",
    "    if filename:\n",
    "        plt.savefig(filename, format=\"png\")\n",
    "\n",
    "\n",
    "# draw enhanced path on the graph\n",
    "def draw_enhanced_path(G, path_to_enhance, node_names={}, filename=None):\n",
    "    path_edges = list(zip(path,path[1:]))\n",
    "    pos_nodes = nx.spring_layout(G)\n",
    "\n",
    "    plt.figure(figsize=(5,5),dpi=300)\n",
    "    pos_nodes = nx.spring_layout(G)\n",
    "    nx.draw(G, pos_nodes, with_labels=False, node_size=50, edge_color='gray')\n",
    "    \n",
    "    pos_attrs = {}\n",
    "    for node, coords in pos_nodes.items():\n",
    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
    "        \n",
    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')\n",
    "    nx.draw_networkx_edges(G,pos_nodes,edgelist=path_edges, edge_color='#cc2f04', style='dashed', width=2.0)\n",
    "    \n",
    "    plt.axis('off')\n",
    "    axis = plt.gca()\n",
    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
    "    \n",
    "    if filename:\n",
    "        plt.savefig(filename, format=\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iXiFWQ1juLmW"
   },
   "source": [
    "### Shortest path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "pnrTW0IDl9ch"
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "nodes = {1:'Dublin',2:'Paris',3:'Milan',4:'Rome',5:'Naples',6:'Moscow',7:'Tokyo'}\n",
    "G.add_nodes_from(nodes.keys())\n",
    "G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(5,6),(6,7),(7,5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "dGxxPrvZp_Ge",
    "outputId": "523e5744-a570-4ee6-a2cb-4dd22458805e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 4, 5, 7]\n"
     ]
    }
   ],
   "source": [
    "path = nx.shortest_path(G,source=1,target=7)\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 874
    },
    "id": "dAdKBv_HjvmM",
    "outputId": "fe53026a-29ad-48a2-9914-43c6da4da20f"
   },
   "outputs": [
    {
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4AAAg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\nG40HAAA8xGazae/evUpPT9fx48ft7oV7VaqL4YIKbCFur2tMv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tbMuWLTFq1KioqKiIiIhFixZFVVVVRERUFjSPD3qcEH1nvBy9Jj8VB37v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121fbfcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_enhanced_path(G, path, node_names=nodes,filename='shortest_path.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dBg6m9ZATaJZ"
   },
   "source": [
    "### Characteristic path lenght"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "DJYfa7CtTZhr",
    "outputId": "03056ea9-7c7c-470b-e166-70d76ec990f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1904761904761907"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_shortest_path_length(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uC7OadvGulnu"
   },
   "source": [
    "### Efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GqjWanR1orDB",
    "outputId": "58b955a2-4f71-473b-a04a-3a08722b6929"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6111111111111112\n",
      "0.6666666666666667\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "nodes = {1:'Dublin',2:'Paris',3:'Milan',4:'Rome',5:'Naples',6:'Moscow',7:'Tokyo'}\n",
    "G.add_nodes_from(nodes.keys())\n",
    "G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(5,6),(6,7),(7,5)])\n",
    "\n",
    "print(nx.global_efficiency(G))\n",
    "print(nx.local_efficiency(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "BKY3H3CsuwH5",
    "outputId": "0bc3d493-9dac-4af3-ffef-834c5b4f8b72"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lCIIg/P39idLSUoIgCKK6uprcrqioiDA1NSWePHlCEARBnD59mnzugw8+IGJi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\nArb15OBB7kk5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1220158d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# higher efficiency\n",
    "G = nx.complete_graph(n=7)\n",
    "nodes = {0:'Dublin',1:'Paris',2:'Milan',3:'Rome',4:'Naples',5:'Moscow',6:'Tokyo'}\n",
    "\n",
    "ge = round(nx.global_efficiency(G),2)\n",
    "\n",
    "# place the text box in axes coords\n",
    "ax = plt.gca()\n",
    "ax.text(-.4, -1.3, \"Global Efficiency:{}\".format(ge), fontsize=14, ha='left', va='bottom');\n",
    "\n",
    "draw_graph(G,node_names=nodes,filename='efficiency.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "id": "rQNK_3WBvi-G",
    "outputId": "dc7abae0-2106-41e7-aa07-cdd1c30108d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LIx9qamoYOXIkADNnziQ0NJSBAwcyd+5cGhsb2blzJ9XV1URERNC9e3dGjhyJXq/n7Nmz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12492fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lower efficiency\n",
    "G = nx.cycle_graph(n=7)\n",
    "nodes = {0:'Dublin',1:'Paris',2:'Milan',3:'Rome',4:'Naples',5:'Moscow',6:'Tokyo'}\n",
    "\n",
    "le = round(nx.global_efficiency(G),2)\n",
    "\n",
    "# place the text box in axes coords\n",
    "ax = plt.gca()\n",
    "ax.text(-.4, -1.3, \"Global Efficiency:{}\".format(le), fontsize=14, ha='left', va='bottom');\n",
    "\n",
    "draw_graph(G, node_names=nodes,filename='less_efficiency.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K2xlwGTQypcZ"
   },
   "source": [
    "### Clustering coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "1BuMAkinyt8-"
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "nodes = {1:'Dublin',2:'Paris',3:'Milan',4:'Rome',5:'Naples',6:'Moscow',7:'Tokyo'}\n",
    "G.add_nodes_from(nodes.keys())\n",
    "G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(5,6),(6,7),(7,5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "IKa1VRNnVmkw",
    "outputId": "bc4c0b9f-d1d5-4f4d-b5b1-b1b5d7bedca9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666667"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_clustering(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "id": "JZ1LQZ6Frq0a",
    "outputId": "f39c2a66-832d-401c-f6e7-f993eaae869b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 1.0,\n",
       " 2: 1.0,\n",
       " 3: 0.3333333333333333,\n",
       " 4: 0,\n",
       " 5: 0.3333333333333333,\n",
       " 6: 1.0,\n",
       " 7: 1.0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.clustering(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "lWdp_yD-y0sx",
    "outputId": "eaff0220-a4b1-497e-9137-0e351597f5ac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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16waFQoF+/foZuywik8RQJDITGo0G586dQ2pqKuzt7aFQKNC/f39OU0X0OwxF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\nD6BNQ3sQTrSdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122000438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc = nx.clustering(G)\n",
    "node_size=[(v + 0.1) * 200 for v in cc.values()]\n",
    "draw_graph(G, node_names=nodes, node_size=node_size,filename='clustering.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ga57kYOA1h47"
   },
   "source": [
    "### Centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "VV2e-FNe1kWf"
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "nodes = {1:'Dublin',2:'Paris',3:'Milan',4:'Rome',5:'Naples',6:'Moscow',7:'Tokyo'}\n",
    "G.add_nodes_from(nodes.keys())\n",
    "G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(5,6),(6,7),(7,5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xj38Q2pq0unD",
    "outputId": "be9722b5-1a65-42ac-ea03-36977f3c0d43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.3333333333333333,\n",
       " 2: 0.3333333333333333,\n",
       " 3: 0.5,\n",
       " 4: 0.3333333333333333,\n",
       " 5: 0.5,\n",
       " 6: 0.3333333333333333,\n",
       " 7: 0.3333333333333333}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "dCuRzRQc1zty",
    "outputId": "99d33686-7884-46eb-c9ed-d7e216352d69"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dublin</th>\n",
       "      <th>Paris</th>\n",
       "      <th>Milan</th>\n",
       "      <th>Rome</th>\n",
       "      <th>Naples</th>\n",
       "      <th>Moscow</th>\n",
       "      <th>Tokyo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Degree centrality</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Dublin     Paris  Milan      Rome  Naples    Moscow  \\\n",
       "Degree centrality  0.333333  0.333333    0.5  0.333333     0.5  0.333333   \n",
       "\n",
       "                      Tokyo  \n",
       "Degree centrality  0.333333  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvMMbb7xBfn4+xy9cwRT6EK6BoRRlneLi8rexXMqg/n2DaBgcTvaXH1Lf1eDJ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\n+hVMkYUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1257163c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc = nx.degree_centrality(G)\n",
    "node_size=[(v + 0.01) * 400 for v in dc.values()]\n",
    "draw_graph(G, node_names=nodes, node_size=node_size,filename='deg_centr.png')\n",
    "\n",
    "df = pd.DataFrame(dc,index=['Degree centrality'])\n",
    "df.columns = nodes.values()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "deUN9xP900v2",
    "outputId": "a4cc364c-1fd8-4aba-d225-b9090629f208"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.4,\n",
       " 2: 0.4,\n",
       " 3: 0.5454545454545454,\n",
       " 4: 0.6,\n",
       " 5: 0.5454545454545454,\n",
       " 6: 0.4,\n",
       " 7: 0.4}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.closeness_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "QYqWGlda13Qy",
    "outputId": "d036c7b2-beca-4106-db9e-f07c99122a2a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dublin</th>\n",
       "      <th>Paris</th>\n",
       "      <th>Milan</th>\n",
       "      <th>Rome</th>\n",
       "      <th>Naples</th>\n",
       "      <th>Moscow</th>\n",
       "      <th>Tokyo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Closeness centrality</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Dublin  Paris     Milan  Rome    Naples  Moscow  Tokyo\n",
       "Closeness centrality     0.4    0.4  0.545455   0.6  0.545455     0.4    0.4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7jRg2fVjarbu4XnTPgBURUW0YikRGVqnS4FhGUbPOodUKJJzLNVBFRFQXhiKR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\n/H8l0lULIsvgIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122010780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc = nx.closeness_centrality(G)\n",
    "node_size=[(v + 0.1) * 400 for v in dc.values()]\n",
    "draw_graph(G, node_names=nodes, node_size=node_size,filename='clos_centr.png')\n",
    "\n",
    "df = pd.DataFrame(dc,index=['Closeness centrality'])\n",
    "df.columns = nodes.values()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ts3sMr0H06-d",
    "outputId": "10acf18b-b17a-4079-d254-ef4c1e0042cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.0,\n",
       " 2: 0.0,\n",
       " 3: 0.5333333333333333,\n",
       " 4: 0.6,\n",
       " 5: 0.5333333333333333,\n",
       " 6: 0.0,\n",
       " 7: 0.0}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.betweenness_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "T29a81GV2PFk",
    "outputId": "1e6e102f-d427-4c0f-9d33-108c03223bb7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dublin</th>\n",
       "      <th>Paris</th>\n",
       "      <th>Milan</th>\n",
       "      <th>Rome</th>\n",
       "      <th>Naples</th>\n",
       "      <th>Moscow</th>\n",
       "      <th>Tokyo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Betweenness centrality</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Dublin  Paris     Milan  Rome    Naples  Moscow  Tokyo\n",
       "Betweenness centrality     0.0    0.0  0.533333   0.6  0.533333     0.0    0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GmTNnam0ze/ZsAECXLl0wYcIEbNmypc7+NRoNNm3ahEWLFgG4N6Zv8uTJ+M9//oMtW7ag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\nWgxFIiIirf8P8gd+pqCWs0MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1253daa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc = nx.betweenness_centrality(G)\n",
    "node_size=[(v + 0.1) * 400 for v in dc.values()]\n",
    "draw_graph(G, node_names=nodes, node_size=node_size,filename='bet_centrality.png')\n",
    "\n",
    "df = pd.DataFrame(dc,index=['Betweenness centrality'])\n",
    "df.columns = nodes.values()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8TM-zF2C_OFA"
   },
   "source": [
    "### Assortativity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "XXa6_QaTe-Cp",
    "outputId": "3c5089ae-1a96-440d-b385-36ef53ccbc24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree_pearson_correlation_coefficient(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "lzZwqxIy2Rek",
    "outputId": "b752fc67-7d58-4d71-e99d-36033e415815"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EY9Dnn38OpVIJiUSCxMREJCUlXfZvJJ07dw7R0dG3NAKzoqIC77zzjuGa4r/+9S8EBgbe\nco2PPfYYHBwckJeXd0OvP3fuHHbu3Im//vWv8PX1xQ9+8AMkJiYyEK2YRAghzF0EEY28vLw8JCcn\no7+//7LOKklJSTccGtcikUhQXV1tCLOamhr4+/vjVj5ONm3ahHfffddwbTE7OxvZ2dm3XKNCoUB2\ndvY1g/Vq3WfCwsLYfYYA8PQpkdV58803zV3CZVxdXaHVao2+HZ1OhxMnTiA/Px86nQ7x8fHsPkPD\n8K+ByErU1NQgIyMDM2fOBAB0dnZi+fLliI+PR0xMDDZv3mw40hNCYMuWLYiOjkZ8fDyWLVuGzs5O\nAMC8efMAAAsWLEBSUtJlo0f/9Kc/Yfbs2ZgxYwa2b99ueH7Pnj2YNWsWZs+ejYSEBKhUKsMyNze3\nGw7Fn//85/D29sYzzzyDRx55BJGRkZg/fz6am5uv+vpvvvkGjz32GKKjoxEQEIANGzYgKSkJYWFh\nePDBBzF58mS8/fbbAIDvf//78PDwwI4dOwDAsP/33XcfMjMz0dfXB+D/T9Fu2bIFDz30EDw9PZGd\nnX1D9ZMFEEQ0Jh08eFAAEAkJCSIxMVFERUWJJUuWGJYvW7bM8Li7u1uEhISInJwcIYQQOTk5Iigo\nSGg0GiGEEMuXLxfLli0zrAtAVFdXGx5XV1cLAOKPf/yjEEKIM2fOiHHjxokTJ04IIYTYsWOHaGlp\nMbzWz8/PsO5HH30kXF1dDY+3bdsmEhMTr7lfS5YsEXfeeafo6OgQQgixYsUKsXDhQsPyKVOmiIMH\nD4q+vj6xe/dusWLFCpGTkyPOnDkjkpKSxP79+4UQQvzjH/8Q06ZNM6z35Zdfip/97GdCCCH+8pe/\niMDAQKHRaIROpxNpaWniF7/4xWXbWLp0qeH3nJube816ybLwSJFojDtw4ADy8vLwt7/9zfCcTqfD\nzp07sWzZMgDAuHHjkJ6ejm3btgEAcnJykJ6ebpj/b+nSpdixY8d3HtGlpaUBAPz9/RETE4Pdu3cD\nAMLCwrB06VLEx8cjIyMD58+fR2NjIwDAyckJQghoNJob3qf58+cbbpVYtGgRPvjgA0NtQggcOXIE\nv/nNb9DT0wMbGxu88847WLp0KU6cOIGysjIAQEpKCpqbm1FUVAQA2LFjB55++mkAg9c1FyxYAEdH\nR0gkEixcuNBwBDnk4YcfBjB4jXb+/Pk3XDuNbrymSGQlhgagAEBTUxN6e3vh6elpWO7p6Yna2loA\nQG1t7bBl/f39aGhogI+PzzW38e0b3N3d3VFXVwcAeOihh7Bq1Sr88Ic/BDA4UKe7u9vws62tLRoa\nGjB16tQb2pcrt9Pf34+zZ8+iuroanZ2daG9vx8qVK/H73/8e5eXlOHToEMaNG4eMjAzDdmUyGdLT\n05GTk4N7770XZ86cwV133WXY/127duHgwYMAYAjXb5s4ceIN1UqWhaFIZIU8PT1hb2+PpqYm3H33\n3QAGg9LX1xcA4Ofnh6amJsPrm5qaYGdnB7lcft33bW1thZubG4DBG+FnzJiBxsZG1NTU4IEHHgAw\n2FT7Sra2tqivr7/hUGxtbTX8XFNTA6lUir///e8IDQ2Fs7Mz7rvvPnh5eaGkpAQJCQmGWyyu3Pbi\nxYsxf/58zJo1C3PnzjU87+fnh+9973tYv3694blrXbeksYWnT4mskI2NDRYvXmwYDHPp0iXs3r0b\nS5cuBQBkZGRg9+7duHTpEgBg+/btWLRoEWxtbQEAzs7O6O7uxl/+8hd88MEHhvcdOkV75swZFBUV\n4cknn4S7uztcXFxQXFwMAPj000+H1TN0pHij9u3bh5qaGnz44YfYtGkT7rvvPrzwwguYP3/+ZUd0\nAQEBKC0thU6ng0ajQX5+/mXvEx0dDQ8PD7z44otIT083PJ+RkYH3338fPT09AICDBw9i5cqVN1wf\nWTBzX9QkopG3b98+ERYWZhhos2/fvmGv6ezsFMuXLxdxcXEiKipKvPHGG0Kn0xmWb9myRURHR4u4\nuDixdOlSw8AWIYR46aWXREhIiIiLixNnz54VUVFRAoD49a9/LZKTk0VQUJDIzs42vH7Pnj3C399f\nzJkzR2zcuFEAEFFRUaK4uFiEhYUJe3t7ERERIT788EMxffp0MXHiRPHCCy9cdd+efPJJMWfOHBES\nEiKmT58u5syZI5qamoQQQjz66KPC3t5ehIWFiS+//FLU1dWJpKQkERYWJp566imRlJQkpkyZInbu\n3Gl4v1/84hfioYceGradX/7yl2LmzJkiOTlZPPzww6KhoUEIIcSiRYsM2/j2+9DYwJv3icjs+vv7\n8eabb+JHP/qR4Wj0SmfPnkV+fj7eeusthIaG4p133oFMJrvtbb/zzjvw8PAwDBIi68bTp0Rkdr06\nCc7Z+eLlPWr8rfQcunoHAAyOJD116hS2bduGDz/8EIGBgbj77rvh5+d324GYk5MDYLC1XGpq6m3v\nA40NPFIkIrMqrWlFRnYJ+vsH0KeTwFFmCwmATUmeuPjVl5d1n3n11Vfx+9//Hg4ODvjpT3+K5cuX\n3/J2Y2Nj0dPTg5UrV/J6IRkwFInIbLp6BxD1+n5oeoff/yiT6PD+9+9CaND0W2o2TnQrePqUiMwm\n98gFXOtrudTODlXdjgxEMimGIhGZzVd1bejuu3qXnO4+LY7X8t5AMi3evE9EJnfx4kUUFBTgdMU3\nkNn4oE83/GjQ3gY4f6wM712sQHh4OIKDg0dktCnR9fCaIhGZTF1d3WAYnj6Ne+65BzOU9+D+/ym6\n6jVFJ3tbFGYl48K5aqjVapw7dw533303wsPD4evry9OqZBQMRSIyKiEEqqurUVBQgMbGRkRHR+Oe\ne+6Bvb09gP8ffSrE4ClTR5ktJBIgO2MmIhVuhvfp7OzE4cOHoVarYWNjg/DwcISFhcHJyclcu0Zj\nEEORiIxiaFJflUqF/v5+xMbGIiQk5Koz3Gt6B5B75AJqWrqhcHdESqgPnOyvfnVHCIFz586hoqIC\nJ06cgL+/P8LDwxEQEMAJg+m2MRSJaEQNDAygoqIChYWFcHR0RFxcHKZPN85tFb29vaisrIRarUZH\nRwfCwsKgVCrh7u4+4tsi68BQJKIR0dPTg9LSUpSUlGDSpEmIi4vDHXfcYbJrf42NjVCr1Thy5Ag8\nPT0RHh6OoKAg2NnZmWT7NDYwFInotnR0dKCoqAgVFRWYNm0aYmNj4eXlZbZ6tFotvvrqK6jVapw/\nfx5BQUGIiIiAj48PB+fQd2Io3oJt27Zh2bJl4K+OrFlTUxMKCgpQVVWFsLAwxMTEjLqJdzs6OgyD\nc+zs7KBUKhEaGsrBOXRNDMWb1NPTg3vvvRfHjh1jKJJVOn/+PFQqFWpraxEZGYmZM2caJvEdrYQQ\nOHv2LNRqNU6ePImpU6ciPDwcd955Jwfn0GUYijdpy5Yt6O3txU9/+lOGIlmNodkqVCoVOjo6EBsb\nC6VSaZHX63p6egyDczo7O6FUKhEeHg5XV1dzl0ajgMWHYlZWFrZu3Yq33noLGRkZWLlyJbZv345P\nP/0Ud999NzIyMtDT04P+/n6kpqbipZdeAgCUlZVh7dq1kEgkkEql+N3vfofAwEBs3boVr732GqKj\nozF+/HiUlpbCxcUFeXl5aGtrwyOPPIL33nsPd955J0ORxjytVoujR4+ioKAAtra2iIuLQ1BQ0Jg5\numpoaIBarcbRo0fh5eWF8PBw3H333RYZ9jQyLD4UASApKQkZGRnIyMgAACgUCmRnZ+OTTz6Bu7s7\nXnrpJWg0GsydOxf5+flob29HQEAA/v73v+P+++/Hxx9/jBdffBEnTpyAjY0NXn75ZfzhD3/A0aNH\n4e7ujg0bNmDz5s146aWXkJCQgODgYPj7+zMUaczq6+tDWVkZioqK4O7ujri4OEydOnXMDlQZGBjA\nyZMnoVarceHCBQQHByM8PByTJk0as/tMVzeme5+6ublh7969SElJQXBwMPbt2wcAyM3NhbOzM+6/\n/34AwPz58/HUU0+huLgYMTExAICYmBh4enoCADZv3oza2lpUVFRg8+bNqKmpMcv+EBmbRqNBcXEx\nysrKoFAokJ6eDh8fH3OXZXRSqRTBwcEIDg5Ge3s7Kioq8P7778Pe3h7h4eEICQmBo6OjucskExjT\nobh+/Xo4OTkhPT0dUqkUGzduRFpaGmpra9Ha2oqkpCTDaz09PdHS0mJ4fOUoupdffhmbNm0yVelE\nJjXUoLuyshLBwcFYvnw53NzcvnvFMWjixIlITExEQkICqqurUVFRgYMHDyIgIADh4eHw9/cfM6eP\nabgxEYoymQy9vb2Gx21tbQAGb+bNzMxEZmYm9u/fj5SUFERERMDPzw++vr7Iy8szrNPR0QEHB4dr\nbqO8vBxff/01gMEL9cDgadvExES88sorRtgrIuOrq6uDSqXCmTNncM8992DVqlVwdnY2d1mjgkQi\nwdSpUzF16lRcunQJlZWVOHDgALq7uxEWFobw8HC4uLiYu0waYWMiFP39/VFZWQkAOHToELq7uwEA\nGzZswJo1a6BUKhEVFQWZTAYhBFJSUrB27VqUlpYiMjISGo0GycnJ+PTTTw2nTK9UXl5u+Lmmpgb+\n/v6XhSqRpRhq0K1SqdDU1ITo6GikpqYaGnTTcOPGjUNkZCQiIyNRX18PtVqNrVu3YtKkSVAqlbj7\n7ruv2tOVLM+YGGhTVVWFtLQ0uLm5ITU1FW+//TZcXFyQlpaGzz77DFKpFO3t7ViyZAlWr14NYHD0\n6bp16yCEgBACWVlZSElJwa5du/DjH/8YPT09mDNnDnJyci7b1tatW/Hee++huLgYiYmJePHFF/HQ\nQw+ZY7eJbsrVGnSHhobC1tbW3KVZpIGBAVRVVUGtVqOurg4zZswwDM4hyzUmQpGIrq2/vx+HDx9G\nQUEBnJ2dERcXh2nTpnFU5Qhqa2tDRUUFKioqMG7cOMPgnNHe1ICGs9pQ7BqaqqZZA4WHE1JCfeB8\njalqiCzRpUuX8OWXX6KkpAQ+Pj6GBt1kPDqdDtXVg5Mif/3117jrrrsMg3P4JcQyWGUo3uikpkSW\naLQ16LZW3d3dOHr0KNRqNXp7e6FUKqFUKkddf1i6nNWFYlfvAKJe3w9Nr3bYMid7W5RsmH3NyU2J\nRrNvN+hWKpWIjo7mB/AoIIRAXV0d1Go1jh07Bh8fH4SHh2P69OkcnDMKWV0o/q30HH6eexzdfcND\n0VFmi00pQUiP5CkmshzfbtA9c+ZMREZG8lrWKNXf328YnNPQ0IAZM2YgIiICcrnc3KXdkpycHKxa\ntQpHjx6FQqEwdzkjwuq+ptQ0a64aiMDgqdTqZo2JKyK6eUIIfPXVV1CpVOjs7ERsbCwef/xx9uwc\n5ezs7BASEoKQkBBcvHgRarUau3btgrOzM5RKJUJCQq57v/Ro8qMf/QiOjo7o6uoydykjikeK32In\n0SFxfBOejhmcVoannmi0GesNuq2RTqfDmTNnoFarcfr0aUyfPh3h4eGYMmXKqB6cU1tbC19fX0gk\nElRXV4+ZI0WrC8Xvuqb40bIQVFUeRmVlJSZPnoyIiAhMmzaN93KRWVlbg25r1d3djSNHjkCtVqO/\nv98wOGfChAk3tL4pZw0awlAcA25k9Gl/fz+OHz+O8vJytLa2Gto6ubu7m7l6siZXNuiOi4uzigbd\n1k4IgQsXLhgG5/j6+hoG53zXF3RTzRo0ZKyFotVdUwSASIUbSjbMHrxPsaUbCndHpIT6XDbq1M7O\nDmFhYQgLC0NzczPKy8uxbds2eHp6IiIigm2dyKjYoNu6SSQSTJ48GZMnT8bcuXNx/PhxlJSU4OOP\nP0ZoaCjCw8Nv+jabkZw1aCyz2k91J3vpDY8y9fDwwJw5czBr1iycPHkS5eXl2Lt3L0JCQix65BiN\nPmzQTVf69hf01tZWqNVq/OUvf8GECRMQHh6OGTNm3FDf2pGcNWgss9pQvBW2trYICgpCUFAQ2tra\noFarsXPnTkyYMAERERGYMWMGZDKZucskC8MG3XSj3NzcMGvWLCQnJ+Prr79GRUUFPv/8cwQGBiI8\nPBx33HGHSWYNGssYirfIxcUFycnJSExMxNdff43y8nJ8/vnnCAoKQkREBHx8fDgIgq7rygbdcXFx\nCAkJ4aCKy8snAAAcP0lEQVQu+k42NjaYNm0apk2bBo1Gg8OHDyM3Nxc6nQ7jxo0zzOpjrFmDxjKr\nHGhjLJ2dnaioqIBarYZMJkNERASbAtMw/f39qKioQGFhIRt004gRQuCbb77Bv/71L/z85z+Hq6sr\nUlNTsXv3bqPMGvTnP/8ZO3bswKFDhxAVFYUHH3wQP/vZz8y1+yOGoWgEQgjU1NSgvLwcp06dspj7\njsi4Ll26hNLSUpSUlGDy5Mls0E1G09fXh+PHj0OtVqOlpQWhoaGIiIiAh4eHuUsb9RiKRjZ031F5\neTl0Oh3Cw8MRFhbGwRNWpKOjA4WFhaioqMD06dPZoJtMqrm5GRUVFTh8+DBcXV2hVCoRHBw87Jo1\nZw4axFA0ESEEamtrUV5ejqqqKvj7+yMiIgJTp05lN5Ixig26aTTR6XQ4deoU1Go1zp49axic4+fn\nhy/PXuTMQXoMRTPo7e3F0aNHUV5eju7uboSHh3NKmTHk3LlzUKlU+Oabb9igm0alrq4uHD58GGq1\nGn06CbY2KtBzlZbQ1jhzEEPRzOrq6lBeXn5Z1wq2lbM8327Q3dXVhZiYGCiVSjboplFNCIF3PjuM\nt76oRZ9u+HgHa5w5yHrif5SaNGkS5s+fjzlz5uD48eMoKirCJ598grCwMERERLCLySjHBt1kySQS\nCTqE/VUDERg8lVrT0m3iqsyLoThKXK2t3J///Gd4eXmxrdwo1Nvbi/LychQVFcHDwwNz585lg26y\nSAoPJzjKbK85x6zC3dEMVZkPT5+OYlqt1jAhaV1dnaGtHEcums+3G3T7+/sjNjaWDbrJon3XzEG8\npkij0tCEpBUVFZg4caKh5yHbyplGa2srCgoKcOzYMQQHByM2NpantmnMuJGZg6wFQ9HC6HQ6Q1u5\ns2fPsq2ckX27Qfe9996LmTNn8h5TGpM0Q/cpXmPmIGvBULRgQ23lysvLYW9vz7ZyI+TbDbqbm5sR\nHR2NiIgINugmsgIMxTFg6ENcrVYb2spFRETgjjvu4NHjTdDpdDh+/DgKCgrYoJvISjEUx5ju7m4c\nPnwY5eXlEEIYGgM4OTmZu7RRiw26iWgIQ3GM+nZbuRMnTmDq1KlsK3cFNugmoisxFC3Y559/jvXr\n1+Pw4cNISEgYdmQzNGloT08PKisrL2sr5+HhgeXLl6O4uBg3+yfw+uuvY2BgAD/96U9HalcAAM8/\n/zx27dqFt956CxkZGZct6+vrw5w5c3Do0CFUV1dDoVDc8nba29tRVFSEiooKBAYGIiYmhre5EBEA\nhqLFy8vLQ3JyMvr7+y+7uT8pKemymbSHDLWVq6yshIODA9asWYOBgYGbum7W29sLIYRRZuZOSkpC\nRkbGsFAcIpFIbjkUm5qaoFKpcPLkSSiVSsTExGDChAm3VzARjSnWN97WSrz55ptXff7bbeU+//xz\nAMBbb711U23lLG0U5pUNulevXs0RukR0Vby4NMbU1NQgIyMDM2fOBADs2bMHs2bNwuzZs5GQkACV\nSgVgsK1cUFAQAEAmk2H9+vWYPn06fvKTn+Do0aMYGBjAK6+8gpiYGCQnJyM9PR11dXX4/PPPERgY\niKSkJMM2T506hQceeAAJCQmIjY3F3r17AQAlJSVQKpVQKBTYsmULEhMTERoaiq+++uq6+3Dq1Cmk\npqYiPDwcixYtQnf31XsvCiGwZcsWREdHIz4+HsuWLUNnZ6dh2WuvvYbJkydj9uzZKCsrw/r165GV\nlYWmpiZotVqsWbMGISEheOCBB7Bp0yY4ODjgscceAzA4i8CyZcsQHx+P2NhY/OEPfwAwGLDR0dGQ\nSCTIzs7GnDlzYG9vj5qamlv7DyOi0UWQRTt48KAAIBISEkRiYqKIiooSS5YsMSzfsWOHaGlpEUII\nUV1dLfz8/AzLqqurBQDx97//XQghxOuvvy5iY2NFTk6OWLdunfDz8xP19fVCCCHWrFkjDh48KIQQ\nYtu2bSIxMVEIIUR/f7+YPn262LZtmxBCiFOnTonx48eLr7/+2lCfnZ2d+OKLL4QQQjz33HPi2Wef\nveb+JCYmisTERNHX1ye0Wq144IEHxIYNGwzLAYjq6mohhBA5OTkiKChIaDQaIYQQy5cvF0uXLhVq\ntVps3LhR2NnZiU8//VRotVrx29/+9rJ1f/e734nQ0FBx6dIlodPpxBNPPCGmTJli2M4zzzwjFi9e\nLIQQoqOjQ/j7+xv2Yej3tn37diGEEL/61a/EhQsXrv8fRUQWgUeKY8SBAweQl5eHv/3tb5c9HxYW\nhqVLlyI+Ph4ZGRk4f/48GhsbL3vNAw88AAAIDw9HU1MTFi1ahCVLlqCtrQ0vvfQS/vjHP2LhwoWG\no89vKy4uxpkzZ/D0008DAAICAhAVFYWdO3caXuPs7Iz4+HgAQGhoKKqrq6+7Lw8//DDs7OxgY2OD\np556atg+DcnJyUF6ejocHR3R29uLyMhI5OTk4PDhw+jq6sJ9992HuXPnwsbGBt///vcvW/f999/H\nE088AQcHB0gkEixcuNCwTKfTYceOHVi2bBkAYPz48UhNTcWOHTuG1QkAL774IiZNmnTdfSIiy8Br\nimOMQqFAdna24fFDDz2EVatW4Yc//CGAwYEqV56OHBpsYm9vj76+PgBASEgIPvvsM7zxxhv4yU9+\ngpkzZyI6OhpKpRIXL140rFtbWwtXV9fLBvl4enqitrZ22PsDgIODg2Eb1+Lq6mr42d3dHXV1dVd9\nXW1tLSZMmIADBw6grKwMDg4O0Gq1+N73vofCwkJ4eHgYXnvltdK6urprLm9qakJvby+ysrIM1x7b\n2tqgVCovew9OCk009jAUx7DGxkbU1NQYjgT7+/tveN3u7m4EBQXhww8/RH19PR577DFoNBpMnDgR\npaWluHDhAkpKSuDl5YWLFy9iYGDAEIxNTU0IDAy85bpbW1sNPzc3N1/1KKy1tRUODg74+OOPcddd\nd+GZZ57BiRMnYGdnB7lcjkmTJl127bKlpeWy9SdNmoSmpqarLvf09IS9vT3efvttREZGAhj83V3r\n2iYRjR08fTqGubu7w8XFBcXFxQCATz/99IbXLSkpwaZNmwAA3t7emD59OqRSKRISEvC9730Pbm5u\nOHfuHAoLCyGXy/Hb3/4WQgicOXMGxcXFw05X3ox//vOf6O/vh06nw86dO7FgwYLLln/88cd49913\n8eCDD+Kbb77B/fffDzc3N2zfvh2LFi2Cra0tnnjiCRQWFuLMmTMAgN27d1/2Hk8++SQ++OAD9PT0\nQAiB999/37DMxsYGixcvvux06auvvoqcnJxb3icishDmvqhJt27fvn0iLCzMMNBm3759w16zZ88e\n4e/vL+bMmSM2btwoAIioqCjR0tIioqKiBADx0EMPibNnz4qwsDBhb28vFi1aJOrq6kRaWppISEgQ\nsbGx4tFHHxUXL14U+/btE9OnTxcTJ04UL7zwgtBoNGL37t0iKChI3HnnnWLGjBnin//8pxBCiGPH\njhne89lnnxXFxcWGddevXz+s1ueee05MnDhRrF27VsydO1eEhYWJ73//+6Krq0scP35cBAYGCgAi\nODhYnD59WgghxJYtW0R0dLSIi4sTS5cuFR0dHYb3e++990RQUJCYNWuW+NOf/iQAiJqaGiHE4ACh\n1atXi+DgYDFv3jzx+uuvC4VCYVi3s7NTLF++XMTExIiEhATxgx/8QAwMDFz2e0tMTBTHjh0b0f9T\nIjIv3rxPI0IIgfPnz0OtVuPEiRO48847DW3lbrWH6FCDbpVKBa1Wi9jY2Jtq0N3a2mq4VtjU1AS5\nXI6uri44Ojqip6cHOp0Ojo6Ds4q///77+OUvf2k4qiYi68RQpBHX09ODo0ePQq1WG9rKhYeHD+se\n0zU0f1uzBgoPJ6SE+sDZXjoiDboHBgaQlJSE//znP7CxscGvf/1r/O///i/+85//AAD279+PAwcO\n4PXXX4dOp8Pjjz+OkJAQ/PznPx/R3wURWRaGIhnVt9vK+fn5ISIiAnfddRfKz7cPn+kbwJpwGdq/\nLoevry9iY2NvuUG3EAJLlizByZMnYWdnB2dnZ/zhD38wtIerqanBs88+i56eHvT29kKpVOKtt95i\npxsiK8dQJJPo6+vD8ePHUV5ejoaWdmxvn4Ye7fDX2dsI7Hv+XkyZ7G36IonI6nH0KZmETCaDUqnE\nsmXL4HHPHFzrq5itVIqiC9e/j5GIyFgYimRyNc0a9Oquvqy7T4uaFt4PSETmwZv3ySSE/h7G/Px8\nNHyjhb2tHL1XOX3qKLOFwt3R9AUSEYGhSEam0+lQVVWF/Px89Pf3Iz4+Ho+kBSL2zYPo1Q5PRYkE\nSAn1MUOlREQcaENGotVqceTIEahUKjg4OCA+Ph7Tp0833FZRWtOKjOwSaLUCPQO6wdGnEiA7YyYi\nFd89pyMRkTEwFGlE9fX1oaysDEVFRfDw8EB8fDwUCsVV7zHU9A7gQ/V5/P2Tf2PB/Nl4SDkZTvY8\neUFE5sNPIBoR3d3dKCkpQWlpKRQKBdLT0+Hjc/3ToE72UjwV7Y+LZT1InmLPQCQis+OnEN2Wjo4O\nFBYWoqKiAoGBgVi6dOllUzLdCG9vbzQ0NMDbm/cmEpF5MRTplrS0tEClUuHEiRNQKpV47rnnhrVx\nu1FeXl5oaGgY4QqJiG4eQ5FuyoULF6BSqVBTU4PIyEhkZmYammrfKm9vbxQVFY1QhUREt46hSN9J\nCIGamhrk5+ejqakJMTExePjhhyGTyUbk/eVyOY8UiWhUYCjSNQkhcPLkSeTn56OnpwdxcXEICQmB\nVDqyfzbjx4+HTqdDV1cXnJ2dR/S9iYhuBkORhtFqtaisrIRKpYJUKkV8fDwCAwNhY2OcroASiQRy\nuRz19fUICAgwyjaIiG4EQ5EM+vv7oVarUVBQAFdXV8ydO/e2Jgm+GUOnUBmKRGRODEVCT08PSkpK\nUFJSAj8/PzzxxBPw9fU1aQ1yuRzV1dUm3SYR0ZUYilass7MTRUVFUKvVmDZtGpYsWQJPT0+z1CKX\nyzkClYjMjqFohVpbW1FQUIBjx44hNDQUzz77LFxcXMxak5eXF1pbWzEwMDDiA3mIiG4UP32sSH19\nPVQqFU6fPo17770XL7zwApycnMxdFgBAKpXC1dUVzc3N7GxDRGbDULQCZ8+ehUqlQl1dHaKjo5GS\nkgJ7e3tzlzXM0GAbhiIRmQtDcYwSQuDUqVPIz89HV1cX4uLi8OSTT47qU5NDt2WEhYWZuxQislKj\n9xOSbolOp8OxY8eQn58PiUSC+Ph4BAUFGe0ew5HEwTZEZG4MxTFiYGAAFRUVKCgowPjx4zF79mwE\nBASY5B7DkTJ0pCiEsKi6iWjsYChauN7eXpSWlqK4uBg+Pj545JFHcMcdd5i7rFsyfvx4CCHQ1dWF\n8ePHm7scIrJCDEULpdFoUFRUhLKyMgQEBODpp5+GXC43d1m3ZajdW0NDA0ORiMyCoWhh2traUFBQ\ngKNHj2LGjBlYsWIFXF1dzV3WiGG7NyIyJ4aihWhsbIRKpcKpU6cQERGBVatWjckZJby9vXHmzBlz\nl0FEVoqhOMrV1tYiPz8ftbW1iIqKwrx58+Dg4GDusoxGLpejsLDQ3GUQkZViKI5CQgicPn0a+fn5\naG9vR2xsLB5//HHY2dmZuzSj8/T0ZLs3IjIbfuqMIjqdDidOnEB+fj60Wi3i4+MRHBwMW1tbc5dm\nMmz3RkTmxFAcBQYGBnDkyBGoVCo4OjoiKSkJ06ZNs9p79YbuV2QoEpGpMRTNqK+vD2VlZSgsLIRc\nLkdqaiqmTJlitWE4ZGgEKhGRqTEUb0JfXx82bNiAoqIi9PT0wMnJCR988AG8vLxu6n26u7tRXFyM\nL7/8Ev7+/li4cCEmTZpkpKotDwfbEJG5MBRvwtq1axEUFIRf/epXAIDnnnsOGo3mhtdvb29HYWEh\nDh8+jKCgICxbtgzu7u7GKtdieXt7o6Ghge3eiMjkJEIIYe4iLEFDQwMiIyNRU1Nz0821m5uboVKp\ncPLkSSiVSsTExLBjy3UIIbBlyxY899xz/D0RkUmN/qkTvkNWVhZcXFyQnZ0NAFi5ciUcHByQl5eH\nhoYGzJs3D8nJyYiPj8fmzZsN65WVlSEhIQGJiYmYNWsWqqqqAABbt26FQqHAggULsGLFCiiVSiQl\nJeHQoUNQKBR45ZVXEBcXhzlz5uCLL764bm0XLlzA7t27sW3bNri4uCAzMxNz5szhB/13kEgkhqNF\nIiKTEmNAYmKi2LZtm+HxlClTxMGDB8X69evFG2+8IYQQoqurS8TFxQkhhGhraxMeHh7iwIEDQggh\ncnNzxbRp04RWqxVCCLFp0yYhl8tFY2Oj0Gq1IisrS2zevFlIpVLx3//930IIIQ4dOiQcHBxETU3N\nZbXodDpx+vRpsX37dvHrX/9aFBYWit7eXmP/CsacTz/9VHzxxRfmLoOIrIzFHylej5ubG/bu3Ytj\nx47ByckJ+/btAwDk5ubC2dkZ999/PwBg/vz5qK+vR3FxsWHdmJgYeHp6wsbGBps3b0Zvby9sbW2x\natUqAEBCQgLCw8Oxc+dOAIOn/E6cOIF3330Xe/fuRWhoKFavXo3o6GjIZDIT77nlk8vlaGxsNHcZ\nRGRlxvRAm/Xr18PJyQnp6emQSqXYuHEj0tLSUFtbi9bWViQlJRle6+npiZaWFsPjiRMnXvZerq6u\ncHNzu6yrjK+vL86fP4+KigqoVCrIZDLEx8cjMDCQA0Ruk1wuR0FBgbnLICIrMyZCUSaTobe31/C4\nra0NwGAT7czMTGRmZmL//v1ISUlBREQE/Pz84Ovri7y8PMM6HR0d1+0pqlQqL2s/1tfXh9OnT6Or\nqwtHjx7FvHnz4O/vzzAcIZ6enrh48SLbvRGRSY2J06f+/v6orKwEABw6dAjd3d0AgA0bNqCiogIA\nEBUVBZlMBiEEUlJS0NzcjNLSUgCDcxMmJyejvb39mtuIjY1FQEAA3nvvPRw6dAg/+clPcOzYMfz4\nxz/GokWLMHXqVAbiCBpq99bU1GTuUojIioyJr+Br165FWloaEhMTkZqaCh8fH6xZswZpaWlYvXo1\npFIp2tvb8eqrrxrm6fvkk0+wbt06CCEghMArr7wCT09P7Nq1C9nZ2ejp6cHixYuRk5MDYDA4161b\nh1dffRV2dnYYP3489uzZg/j4eHPu+pg2NAKVjQ2IyFR4n+J3aG1thUqlwvHjxxEWFoaYmJhh1xvJ\nOPLz86HRaDB37lxzl0JEVmJMHCneqq7eAeQeuYCaZg0UHk5ICfWBs/3gr6Surg4qlQrV1dW49957\nkZmZCUdHRzNXbF28vb052IaITMpqQ7G0phUZ2SUQAuju08JRZotffHwcr8/1Q+eZCjQ0NCAmJgap\nqamwt7c3d7lWaWi2DMF2b0RkIlYZil29A8jILoGmV2t4rrtv8Od1H51B9iPTDbdxkPk4OztDIpGg\nq6uLXYCIyCTGxOjTm5V75AKudSVVameHWokXA3EUkEgkhqNFIiJTsMpQrGnWGI4Mr3SpT4ualm4T\nV0TXwrkViciUrDIUFR5OcJTZXnWZo8wWCncOqBktGIpEZEpWGYopoT641rgNiWRwOY0OnC2DiEzJ\nKkPR2V6K7IyZcLK3NRwxOsps4WRvq3+e1xNHCw8PD0O7NyIiY7PaT/9IhRtKNswevE+xpRsKd0ek\nhPowEEcZqVQKNzc3NDU1sbMNERmdVSeAk70U6ZF3mLsM+g5D1xUZikRkbFZ5+pQsC2/LICJTYSjS\nqMcRqERkKgxFGvWGQpG964nI2BiKNOoNtXvr7Ow0dylENMYxFGnUk0gkvF+RiEyCoUgWwcvLi6FI\nREbHUCSLwCNFIjIFhiJZBI5AJSJTYCiSRWC7NyIyBYYiWYShdm+NjY3mLoWIxjCGIlkMnkIlImNj\nKJLFuJVQ7Orqwrp165CcnIyEhATce++9+Pe//22kConI0ll1Q3CyLHK5HF9//fVNrVNfX4+ysjLs\n378fUqkU27Ztw6OPPorm5mbY2dkZqVIislQ8UiSLMXRbxs20e/Px8cG7774LqXTw+9/MmTPR0dGB\nixcvGqtMIrJgDEUyi6ysLLi4uCA7OxsAsHLlSjg4OCAvLw8NDQ2YN28ekpOTER8fj82bNwMYbPd2\n4cIFxMfHIzExEbNmzUJVVRUAYOvWrVAoFFiwYAFWrFgBpVKJpKQkODo6IiAgAAAwMDCAd999Fw8/\n/DC8vLzMst9ENMoJIjNJTEwU27ZtMzyeMmWKOHjwoFi/fr144403hBBCdHV1ibi4OCGEEG1tbWL8\n+PFi+/btQgghcnNzxbRp04RWqxVCCLFp0yYhl8tFY2Oj0Gq1Iisry/DeOTk5wsfHR8THx4uGhgYT\n7SERWRoeKdKo4+bmhr179+LYsWNwcnLCvn37AAC5ublwdHSEv78/AGD+/Pmor69HcXGxYd2YmBh4\nenrCxsbGcIQJAIsWLcI333yDpUuXIjo6Gh0dHabdKSKyCAxFGnXWr1+Pxx9/HOnp6VAqlfj4448B\nALW1tdBoNPiv//ovJCUlISkpCZ6enmhpaTGsO3HixOu+97JlyyCRSLB7926j7gMRWSaOPiWzkclk\n6O3tNTxua2sDADQ2NiIzMxOZmZnYv38/UlJSEBERAT8/P/j4+OD555/HqlWrAAAdHR1wcHC45jYK\nCwvh7OyMkJAQw3NOTk7QaDRG2isismQ8UiSz8ff3R2VlJQDg0KFD6O7uBgBs2LABFRUVAICoqCjI\nZDIIIZCSkoK2tjZUVlaiv78fGo0GycnJaG9vv+Y2Tp48ibffftswYrWgoAAnT55EUlKScXeOiCyS\nRAhOZ07mUVVVhbS0NLi5uSE1NRVvv/02XFxckJaWhs8++wxSqRTt7e1YsmQJVq9eDQAoKyvDwoUL\n4e7uDjs7O2RlZSElJQW7du3Cj3/8Y/T09GDOnDnIyckBAJw7dw4vv/wyvvrqK9jY2KC7uxtZWVl4\n8sknzbnrRDRKMRTJ4uzZswcKhQLh4eHmLoWIxhheUySLM8HdCx+o6/BZvQMUHk5ICfWBsz3/lIno\n9vFIkSxKaU0rFr9XjIEBLfqFBI4yW0gkQHbGTEQq3MxdHhFZOIYiWYyu3gFEvb4fml7tsGVO9rYo\n2TAbTjxiJKLbwNGnZDFyj1zAtb7CCTG4nIjodjAUyWLUNGvQ3Tf8KBEAuvu0qGnpNnFFRDTWMBTJ\nYig8nOAos73qMkeZLRTujiauiIjGGoYiWYyUUB9IJFdfJpEMLiciuh0MRbIYzvZSZGfMhJO9reGI\n0VFmCyd7W/3zHGRDRLeHo0/J4mh6B5B75AJqWrqhcHdESqgPA5GIRgRDkYiISI+nT4mIiPQYikRE\nRHoMRSIiIj2GIhERkR5DkYiISI+hSEREpMdQJCIi0mMoEhER6TEUiYiI9BiKREREegxFIiIiPYYi\nERGRHkORiIhIj6FIRESkx1AkIiLSYygSERHpMRSJiIj0GIpERER6DEUiIiI9hiIREZEeQ5GIiEiP\noUhERKTHUCQiItJjKBIREekxFImIiPQYikRERHoMRSIiIj2GIhERkR5DkYiISI+hSEREpMdQJCIi\n0mMoEhER6TEUiYiI9BiKREREegxFIiIiPYYiERGRHkORiIhIj6FIRESkx1AkIiLSYygSERHpMRSJ\niIj0/g9TPqEjaygBIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1254e8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "nodes = {1:'user1', 2:'user2', 3:'Football player', 4:'Fahsion blogger', 5:'user3', 6:'user4',\n",
    "         7:'user5', 8:'user6'}\n",
    "G.add_nodes_from(nodes.keys())\n",
    "G.add_edges_from([(1,3),(2,3),(7,3),(3,4),(5,4),(6,4),(8,4)])\n",
    "\n",
    "draw_graph(G, node_names=nodes,filename='assortativity.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "kY0Jw-j9lON_",
    "outputId": "0b53b25c-031e-4e85-cf30-31f3407169a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.75"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree_pearson_correlation_coefficient(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RbuH3iI1xn7X"
   },
   "source": [
    "### Modularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "Gt2EzDJM_t8b",
    "outputId": "c1003530-c2b0-46a6-8e46-1794d6385082"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3671875\n",
      "0.3671875\n"
     ]
    }
   ],
   "source": [
    "import networkx.algorithms.community as nx_comm\n",
    "\n",
    "G = nx.Graph()\n",
    "nodes = {1:'Dublin',2:'Paris',3:'Milan',4:'Rome',5:'Naples',6:'Moscow',7:'Tokyo'}\n",
    "G.add_nodes_from(nodes.keys())\n",
    "G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(5,6),(6,7),(7,5)])\n",
    "\n",
    "# partitions can be provided manually\n",
    "print(nx_comm.modularity(G, communities=[{1,2,3,4},{5,6,7}]))\n",
    "\n",
    "# or automatically computed using networkx\n",
    "print(nx_comm.modularity(G, nx_comm.label_propagation_communities(G)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DHVDxlT42_TO"
   },
   "source": [
    "### Transitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "qjT3Zp40y32X",
    "outputId": "44a7badd-b088-48b1-b735-9a7dd0977f52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5454545454545454"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.transitivity(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "graph_metrics_packt.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
